Parallel computation of radio listening rates

Obtaining the listening rates of radio stations in function of time is an important instrument for determining the impact of publicity. Since many radio stations are financed by publicity, the exact determination of radio listening rates is vital to their existence and to further development. Existing methods of determining radio listening rates are based on face to face interviews or telephonic interviews made with a sample population. These traditional methods however require the cooperation and compliance of the participants. In order to significantly improve the determination of radio listening rates, special watches were created which incorporate a custom integrated circuit sampling the ambient sound during a few seconds every minutes. Each watch accumulates these compressed sound samples during one full week. Watches are then sent to an evaluation center, where the sound samples are matched with the sound samples recorded from candidate radio stations. The present paper describes the processing steps necessary for computing the radio listening rates, and shows how this application was parallelized on a cluster of PCs using the CAP Computer-aided parallelization framework. Since the application must run in a production environment, the paper describes also the support provided for graceful degradation in case of transient or permanent failure of one of the system's components. The parallel sound matching server offers a linear speedup up to a large number of processing nodes thanks to the fact that disk access operations across the network are done in pipeline with computations

Performance of CAP-specified linear algebra algorithms

. The traditional approach to the parallelization of linear algebra algorithms such as matrix multiplication and LU factorization calls for static allocation of matrix blocks to processing elements (PEs). Such algorithms suffer from two drawbacks : they are very sensitive to load imbalances between PEs and they make it difficult to take advantage of pipelining opportunities. This paper describes dynamic versions of linear algebra algorithms, where subtasks (matrix block multiplication, matrix block LU factorization) are dynamically allocated to PEs. It analyses theoretically the performance of the dynamic algorithms. This paper's contribution is to show that the dynamicpipelined linear-algebra algorithms can be specified compactly in CAP and yet achieve good performance. CAP is a C++ language extension for the specification of parallel applications based on macro-dataflow graphs. The CAP model, based on macro-dataflow graphs, is general and supports pipelining. 1 Introduction The tr..